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class DCGANGenerator(object):
def __init__(self, hidden_dim=128, batch_size=64, hidden_activation=tf.nn.relu, output_activation=tf.nn.tanh, use_batch_norm=True, z_distribution='normal', scope='generator', **kwargs):
self.hidden_dim = hidden_dim
self.batch_size = batch_size
self.hidden_activa... |
('pypyr.moduleloader.get_module')
(Step, 'invoke_step')
def test_run_pipeline_steps_complex_with_run_str_false(mock_invoke_step, mock_get_module):
step = Step({'name': 'step1', 'run': 'False'})
context = get_test_context()
original_len = len(context)
with patch_logger('pypyr.dsl', logging.INFO) as mock_... |
class TDictMixin(TestCase):
def setUp(self):
self.fdict = FDict()
self.rdict = {}
self.fdict['foo'] = self.rdict['foo'] = 'bar'
def test_getsetitem(self):
self.failUnlessEqual(self.fdict['foo'], 'bar')
self.failUnlessRaises(KeyError, self.fdict.__getitem__, 'bar')
def... |
class CustomCallback(TrainerCallback):
def __init__(self, trainer) -> None:
super().__init__()
self._trainer = trainer
def on_epoch_end(self, args, state, control, **kwargs):
if control.should_evaluate:
control_copy = deepcopy(control)
self._trainer.evaluate(eval_... |
class OrgProfileViewTest(TestCase):
def setUpTestData(cls):
add_default_data()
def test_OrgProfileViewOk(self):
response = self.client.get(reverse('org_profile', args=['rap']))
self.assertEqual(response.status_code, 200)
self.assertTemplateUsed(response, 'petition/org_profile.htm... |
def spectral_response_all_junctions(solar_cell, incident_light=None, energy=None, V=0, verbose=False):
science_reference('Nelson pin spectral response', 'Jenny: (Nelson. The Physics of Solar Cells. Imperial College Press (2003))')
if (energy is None):
if (incident_light is not None):
energy ... |
def _wraps(orig, glmfunc=None):
if (glmfunc is None):
glmfunc = orig
def decorator(func):
if ('PYUNITY_SPHINX_CHECK' in os.environ):
return func
if isinstance(orig, str):
if GLM_SUPPORT:
return getattr(glm, glmfunc)
else:
... |
def getDroneMult(drone, src, tgt, atkSpeed, atkAngle, distance, tgtSpeed, tgtAngle, tgtSigRadius):
if ((distance is not None) and (((not GraphSettings.getInstance().get('ignoreDCR')) and (distance > src.item.extraAttributes['droneControlRange'])) or ((not GraphSettings.getInstance().get('ignoreLockRange')) and (dis... |
class JointLoss(nn.Module):
def __init__(self, args: Namespace, device: torch.device, criterion, size_average: bool=True, weights: dict=None, denomitor: float=1e-08):
super(JointLoss, self).__init__()
self.args = args
self.device = device
self.cross_entropy = criterion['ce']
... |
class GlobalAttention(nn.Module):
def __init__(self, dim, coverage=False, attn_type='dot', attn_func='softmax'):
super(GlobalAttention, self).__init__()
self.dim = dim
assert (attn_type in ['dot', 'general', 'mlp']), 'Please select a valid attention type (got {:s}).'.format(attn_type)
... |
class DirLayout():
LAYOUT = ['folder0/file00', 'folder0/file01', 'folder1/folder10/file100', 'folder1/file10', 'folder1/file11', 'file0', 'file1']
def layout_folders(cls):
folders = set()
for path in cls.LAYOUT:
parts = path.split('/')
if (len(parts) > 1):
... |
def random_lil(shape, dtype, nnz):
sp = pytest.importorskip('scipy')
rval = sp.sparse.lil_matrix(shape, dtype=dtype)
huge = (2 ** 30)
for k in range(nnz):
idx = (np.random.default_rng().integers(1, (huge + 1), size=2) % shape)
value = np.random.random()
if (dtype in integer_dtype... |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--module', choices=['all', 'base', 'dplyr'], required=True, help='The module to test')
parser.add_argument('--allow-conflict-names', action='store_true', help='Whether to allow conflict names', default=False)
parser.add_argument('--geta... |
class FakeNotificationAdapter(notification.AbstractNotificationAdapter):
NAME = 'fake'
def __init__(self) -> None:
super().__init__()
self.presented = []
self.id_gen = itertools.count(1)
def present(self, qt_notification: 'QWebEngineNotification', *, replaces_id: Optional[int]) -> in... |
class TestTag():
def test_expired_with_tag_expired_a_minute_ago(self):
now_ms = get_epoch_timestamp_ms()
one_hour_ago_ms = (now_ms - (3600 * 1000))
one_minute_ago_ms = (now_ms - (60 * 1000))
tag = Tag(name='latest', reversion=False, manifest_digest='abc123', lifetime_start_ts=(one_ho... |
def clip_graphs_to_size(data, size_limit=5000):
if hasattr(data, 'num_nodes'):
N = data.num_nodes
else:
N = data.x.shape[0]
if (N <= size_limit):
return data
else:
logging.info(f' ...clip to {size_limit} a graph of size: {N}')
if hasattr(data, 'edge_attr'):
... |
def test_newtype_optionals(genconverter):
Foo = NewType('Foo', str)
genconverter.register_unstructure_hook(Foo, (lambda v: v.replace('foo', 'bar')))
class ModelWithFoo():
total_foo: Foo
maybe_foo: Optional[Foo]
assert (genconverter.unstructure(ModelWithFoo(Foo('foo'), Foo('is it a foo?')... |
class TestHRITDecompress(unittest.TestCase):
def test_xrit_cmd(self):
old_env = os.environ.get('XRIT_DECOMPRESS_PATH', None)
os.environ['XRIT_DECOMPRESS_PATH'] = '/path/to/my/bin'
with pytest.raises(IOError, match='.* does not exist!'):
get_xritdecompress_cmd()
os.environ... |
def tdm_fmcw_tx():
wavelength = (const.c / .0)
tx_channel_1 = {'location': (0, ((- 4) * wavelength), 0), 'delay': 0}
tx_channel_2 = {'location': (0, 0, 0), 'delay': 0.0001}
return Transmitter(f=[(.0 - .0), (.0 + .0)], t=8e-05, tx_power=20, prp=0.0002, pulses=2, channels=[tx_channel_1, tx_channel_2]) |
def _lambert_conformal_conic__to_cf(conversion):
params = _to_dict(conversion)
if conversion.method_name.lower().endswith('(2sp)'):
return {'grid_mapping_name': 'lambert_conformal_conic', 'standard_parallel': (params['latitude_of_1st_standard_parallel'], params['latitude_of_2nd_standard_parallel']), 'la... |
class DarkBlock(nn.Module):
def __init__(self, in_chs, out_chs, dilation=1, bottle_ratio=0.5, groups=1, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
super(DarkBlock, self).__init__()
mid_chs = int(round((out_chs * bottle_ratio)))
... |
class UncaughtError(Redirect):
def __init__(self, view, root_ui, target_ui, exception):
error_source_bookmark = (view.as_bookmark(target_ui) if view else None)
target_bookmark = root_ui.get_bookmark_for_error(str(exception), error_source_bookmark)
super().__init__(target_bookmark) |
def test_bitstruct_signals():
bs = mk_bitstruct('BitStructType', {'foo': Bits1, 'bar': Bits32})
class A2(Component):
def construct(s):
s.in0 = InPort(bs)
s.in1 = InPort(Bits32)
s.out = OutPort(Bits32)
def add_upblk():
s.out = (s.in0.bar + s... |
def plot_amplitudes_zpk(zpks, filename_pdf, fmin=0.001, fmax=100.0, nf=100, fnorm=None):
from pyrocko.plot import gmtpy
p = gmtpy.LogLogPlot(width=(30 * gmtpy.cm), yexp=0)
for (i, (zeros, poles, constant)) in enumerate(zpks):
(f, h) = evaluate(zeros, poles, constant, fmin, fmax, nf)
if (fnor... |
def squad_build_drqa_doc_encodings(out_dir, encoder_model, num_workers, all_squad=False):
print('loading data...')
corpus = SquadRelevanceCorpus()
questions = corpus.get_dev()
if all_squad:
questions.extend(corpus.get_train())
relevant_titles = list(set([q.paragraph.doc_title for q in questi... |
class TestQObjRepr():
.parametrize('obj', [QObject(), object(), None])
def test_simple(self, obj):
assert (qtutils.qobj_repr(obj) == repr(obj))
def _py_repr(self, obj):
r = repr(obj)
if (r.startswith('<') and r.endswith('>')):
return r[1:(- 1)]
return r
def te... |
def test_phased_gaussian_single_particle():
chain = PhasedGaussianSingleParticle(k=(1.2 * 7), sigma=(1.2 / 7), position=(1.5 / 7))
amplitudes = chain.get_amplitudes(sites_count=8)
np.testing.assert_allclose(amplitudes, [((- 0.) - 0.3883731j), (0. - 0.3186606j), (0. + 0.3186606j), ((- 0.) + 0.3883731j), ((- ... |
class ThermalBuilder(BasicBuilder):
def __init__(self, casePath, solverSettings=getDefaultHeatTransferSolverSettings(), templatePath=None, fluidProperties={'name': 'air', 'compressible': False, 'kinematicViscosity': 100000.0}, turbulenceProperties={'name': 'kEpsilon'}, boundarySettings=[], internalFields={}, transi... |
class CLIPVisionConfig(PretrainedConfig):
model_type = 'clip_vision_model'
def __init__(self, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, image_size=224, patch_size=32, hidden_act='quick_gelu', layer_norm_eps=1e-05, dropout=0.0, attention_dropout=0.0, initializer_range... |
class CustomDatasetDataLoader(BaseDataLoader):
def name(self):
return 'CustomDatasetDataLoader'
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
self.dataset = CreateDataset(opt)
self.dataloader = torch.utils.data.DataLoader(self.dataset, batch_size=opt.batchSize, ... |
def _replace_shared_variables(graph: List[TensorVariable]) -> List[TensorVariable]:
shared_variables = [var for var in graph_inputs(graph) if isinstance(var, SharedVariable)]
if any((isinstance(var.type, RandomType) for var in shared_variables)):
raise ValueError('Graph contains shared RandomType variab... |
def test_complete_package_does_not_merge_different_source_type_and_name(provider: Provider, root: ProjectPackage, fixture_dir: FixtureDirGetter) -> None:
project_dir = fixture_dir('with_conditional_path_deps')
path = (project_dir / 'demo_one').as_posix()
dep_with_source_name = Factory.create_dependency('dem... |
def setup(app):
_directive = 'versionremoved'
if (_directive not in versionlabels):
versionlabels[_directive] = 'Removed in version %s'
if (versionlabel_classes is not None):
versionlabel_classes[_directive] = 'removed'
app.add_directive(_directive, VersionChange)
return ... |
class NnetLatticeBiglmFasterRecognizer(NnetRecognizer):
def __init__(self, transition_model, acoustic_model, decoder, symbols=None, allow_partial=True, decodable_opts=None, online_ivector_period=10):
if (not isinstance(decoder, _dec.LatticeBiglmFasterDecoder)):
raise TypeError('decoder argument ... |
def train(data_dir='./data/', embedding_size=300, skipgram=False, siter=10, diter=10, negative_samples=10, window_size=5, output_path='./model', overwrite_compass=True, streamlit=False, component=None):
if (streamlit and (component is None)):
raise ValueError('`component` cannot be `None` when `streamlit` i... |
def test_root_count(root, testapp, add_file_to_root):
resp = testapp.get('/')
resp.mustcontain('PyPI compatible package index serving 0 packages')
add_file_to_root(root, 'Twisted-11.0.0.tar.bz2')
resp = testapp.get('/')
resp.mustcontain('PyPI compatible package index serving 1 packages') |
class TestMultiCorpusDataset(unittest.TestCase):
def setUp(self):
d = mock_dict()
tokens_1 = torch.LongTensor([i for i in range(1, 5000, 2)]).view(1, (- 1))
tokens_ds1 = TokenBlockDataset(tokens_1, sizes=[tokens_1.size((- 1))], block_size=1, pad=0, eos=1, include_targets=False)
self.... |
class Solution():
def minDepth(self, root: Optional[TreeNode]) -> int:
if (not root):
return 0
nodes = [(root, 0)]
depth = math.inf
while nodes:
(node, level) = nodes.pop()
if ((not node.left) and (not node.right)):
depth = min(leve... |
def find_organization_invites(organization, user_obj):
invite_check = (TeamMemberInvite.user == user_obj)
if user_obj.verified:
invite_check = (invite_check | (TeamMemberInvite.email == user_obj.email))
query = TeamMemberInvite.select().join(Team).where(invite_check, (Team.organization == organizati... |
def parse_option():
parser = argparse.ArgumentParser()
parser.add_argument('--width', default=1, type=int, help='backbone width')
parser.add_argument('--num_target', type=int, default=256, help='Proposal number [default: 256]')
parser.add_argument('--sampling', default='kps', type=str, help='Query point... |
def find_all_i2c_power_monitor(i2c_path):
power_sensor = {}
if (not os.path.isdir(i2c_path)):
logger.error("Folder {root_dir} doesn't exist".format(root_dir=i2c_path))
return power_sensor
power_i2c_sensors = {}
items = os.listdir(i2c_path)
for item in items:
path = '{base_pat... |
def install_nvfancontrol(args):
if (not os.path.isfile('/usr/bin/nvfancontrol')):
shutil.copy('tests/nvfancontrol', '/usr/bin/nvfancontrol')
print('Copied test/nvfancontrol')
else:
print('/usr/bin/nvfancontrol already exists')
pytest.exit('I cannot install a fake nvfancontrol! nv... |
class Processor(Iface, TProcessor):
def __init__(self, handler):
self._handler = handler
self._processMap = {}
self._processMap['example'] = Processor.process_example
self._on_message_begin = None
def on_message_begin(self, func):
self._on_message_begin = func
def pro... |
class TFC_RNN(nn.Module):
def __init__(self, in_channels, num_layers_tfc, gr, kt, kf, f, bn_factor_rnn, num_layers_rnn, bidirectional=True, min_bn_units_rnn=16, bias_rnn=True, bn_factor_tif=16, bias_tif=True, skip_connection=True, activation=nn.ReLU):
super(TFC_RNN, self).__init__()
self.skip_connec... |
class TestQueryBestSize(EndianTest):
def setUp(self):
self.req_args_0 = {'drawable': , 'height': 64528, 'item_class': 1, 'width': 8620}
self.req_bin_0 = b'a\x01\x03\x005\x8a\xb4u\xac!\x10\xfc'
self.reply_args_0 = {'height': 2023, 'sequence_number': 41036, 'width': 35260}
self.reply_b... |
class CTOTrainer(NetworkTrainer):
def __init__(self, opt):
super().__init__(opt)
def set_network(self):
self.net = CTO(self.opt.train['num_class'])
self.net = torch.nn.DataParallel(self.net, device_ids=self.opt.train['gpus'])
self.net = self.net.cuda()
def train(self, scaler,... |
def test_float_image():
m = folium.Map([45.0, 3.0], zoom_start=4)
url = '
szt = plugins.FloatImage(url, bottom=60, left=70, width='20%')
m.add_child(szt)
m._repr_html_()
out = normalize(m._parent.render())
tmpl = Template('\n <img id="{{this.get_name()}}" alt="float_image"\n sr... |
def check_accuracy(model, test, device):
total = 0
correct_body = 0
correct_shirt = 0
correct_pant = 0
correct_hair = 0
correct_action = 0
with torch.no_grad():
for item in test:
(body, shirt, pant, hair, action, image) = item
image = image.to(device)
... |
class TestPolicyInformation():
def test_invalid_policy_identifier(self):
with pytest.raises(TypeError):
x509.PolicyInformation('notanoid', None)
def test_none_policy_qualifiers(self):
pi = x509.PolicyInformation(x509.ObjectIdentifier('1.2.3'), None)
assert (pi.policy_identifi... |
def import_catalog(element, save=False, user=None):
try:
catalog = Catalog.objects.get(uri=element.get('uri'))
except Catalog.DoesNotExist:
catalog = Catalog()
set_common_fields(catalog, element)
catalog.order = (element.get('order') or 0)
set_lang_field(catalog, 'title', element)
... |
def parse_args(argv: List[str]) -> argparse.Namespace:
parser = argparse.ArgumentParser(description='copies a file between fsspec locations')
parser.add_argument('--src', type=str, help='fsspec location of the file to read from', required=True)
parser.add_argument('--dst', type=str, help='fsspec location of... |
def do_EQU(op, stack, state):
reg = stack.pop()
(val,) = pop_values(stack, state)
tmp = get_value(reg, state)
if (state.condition != None):
val = z3.If(state.condition, val, tmp)
state.registers[reg] = val
state.esil['old'] = tmp
state.esil['cur'] = val
state.esil['lastsz'] = sta... |
def test_debug_false_by_default(pytester: pytest.Pytester, monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.delenv('DJANGO_SETTINGS_MODULE')
pytester.makeconftest("\n from django.conf import settings\n\n def pytest_configure():\n settings.configure(SECRET_KEY='set from pytest_confi... |
class CocoDistEvalRecallHook(DistEvalHook):
def __init__(self, dataset, interval=1, proposal_nums=(100, 300, 1000), iou_thrs=np.arange(0.5, 0.96, 0.05)):
super(CocoDistEvalRecallHook, self).__init__(dataset, interval=interval)
self.proposal_nums = np.array(proposal_nums, dtype=np.int32)
self... |
def parse_versioned_line(line):
if (line[0] == '#'):
line = line[1:].strip()
line = line.rsplit('#', maxsplit=1)[0]
line = line.split(';')[0].strip()
ops = ['==', '~=', '!=', '>', '<', '>=', '<=']
if any(((op in line) for op in ops)):
for op in ops:
if (op in line):
... |
class Telemetry():
def __init__(self, data, url, shard=None):
self.shard = (shard or ('xbox' if ('xbox-' in url) else 'pc'))
self.events = [Event.instance(event_data) for event_data in self.generate_events_data(data)]
def generate_events_data(self, data):
data_class = SHARD_DATA_MAP[self... |
def completions(config: Config, autoimport_workspace: Workspace, request):
(document, position) = request.param
com_position = {'line': 0, 'character': position}
autoimport_workspace.put_document(DOC_URI, source=document)
doc = autoimport_workspace.get_document(DOC_URI)
(yield pylsp_autoimport_compl... |
class TestErrorTree(TestCase):
def test_it_knows_how_many_total_errors_it_contains(self):
errors = [exceptions.ValidationError('Something', validator=i) for i in range(8)]
tree = exceptions.ErrorTree(errors)
self.assertEqual(tree.total_errors, 8)
def test_it_contains_an_item_if_the_item_... |
def simple_loads(explode: bool, name: str, schema_type: str, location: Mapping[(str, Any)]) -> Any:
value = location[name]
if (schema_type == 'array'):
return split(value, separator=',')
explode_type = (explode, schema_type)
if (explode_type == (False, 'object')):
return dict(map(split, ... |
class _BaseCore():
__metaclass__ = abc.ABCMeta
def __init__(self, hash_func, default_params):
self.default_params = default_params
self.hash_func = hash_func
def set_func(self, func):
func_params = list(inspect.signature(func).parameters)
self.func_is_method = (func_params an... |
def get_diff(repo, base_commit, commits):
print('\n### DIFF ###\n')
code_diff = []
for commit in commits:
for diff_obj in commit.diff(base_commit):
if ((diff_obj.change_type == 'A') and diff_obj.b_path.endswith('.py')):
code_diff.append(diff_obj.b_path)
elif (... |
class TestSubtype(unittest.TestCase):
def test_bit(self) -> None:
assert is_subtype(bit_rprimitive, bool_rprimitive)
assert is_subtype(bit_rprimitive, int_rprimitive)
assert is_subtype(bit_rprimitive, short_int_rprimitive)
for rt in native_int_types:
assert is_subtype(bit... |
class TestVSCF(QiskitChemistryTestCase):
def test_bitstring(self):
bitstr = vscf_bitstring([2, 2])
self.assertTrue(all((bitstr[::(- 1)] == np.array([True, False, True, False]))))
def test_qubits_4(self):
basis = [2, 2]
vscf = VSCF(basis)
ref = QuantumCircuit(4)
re... |
.parametrize('testfile', ['bsrn-pay0616.dat.gz', 'bsrn-lr0100-pay0616.dat'])
def test_read_bsrn(testfile, expected_index):
(data, metadata) = read_bsrn((DATA_DIR / testfile))
assert_index_equal(expected_index, data.index)
assert ('ghi' in data.columns)
assert ('dni_std' in data.columns)
assert ('dhi... |
class TestLogFilter():
def _make_record(self, logger, name, level=logging.DEBUG):
return logger.makeRecord(name, level=level, fn=None, lno=0, msg='', args=None, exc_info=None)
.parametrize('filters, negated, category, logged', [(set(), False, 'eggs.bacon.spam', True), (set(), False, 'eggs', True), (set(... |
def get_mult_function(mult_table, n_dims):
non_zero_indices = mult_table.nonzero()
k_list = non_zero_indices[0]
l_list = non_zero_indices[1]
m_list = non_zero_indices[2]
mult_table_vals = np.array([mult_table[(k, l, m)] for (k, l, m) in np.transpose(non_zero_indices)], dtype=int)
def mv_mult(val... |
.parametrize('from_json', [True, False])
def test_from_json(from_json):
json_path = os.path.join(os.path.dirname(__file__), 'models', 'demand_saving2_with_variables.json')
if from_json:
json_dict = pywr_json_to_d3_json(json_path, attributes=True)
else:
model = load_model('demand_saving2_with... |
def test_do_deterministic():
rng = np.random.default_rng(seed=435)
with pm.Model() as m:
x = pm.Normal('x', 0, 0.001)
y = pm.Deterministic('y', (x + 105))
z = pm.Normal('z', y, 0.001)
do_m = do(m, {'z': (x - 105)})
assert (pm.draw(do_m['z'], random_seed=rng) < 100) |
()
def run(config: EvalConfig):
print('Loading CLIP model...')
device = torch.device(('cuda' if (torch.cuda.is_available() and (torch.cuda.device_count() > 0)) else 'cpu'))
(model, preprocess) = clip.load('ViT-B/16', device)
model.eval()
print('Done.')
prompts = [p.name for p in config.output_pa... |
def node_location(caller):
text = '\n The |cLocation|n of this object in the world. If not given, the object will spawn in the\n inventory of |c{caller}|n by default.\n\n {current}\n '.format(caller=caller.key, current=_get_current_value(caller, 'location'))
helptext = '\n You get... |
class PreviousStateRecorder():
def __init__(self):
self.states = {}
def add_state(self, data_item, slot_values):
dialog_ID = data_item['dialogue_ID']
turn_id = data_item['turn_id']
if (dialog_ID not in self.states):
self.states[dialog_ID] = {}
self.states[dial... |
def rdiff_backup_action(source_local, dest_local, src_dir, dest_dir, generic_opts, action, specific_opts, std_input=None, return_stdout=False, return_stderr=False):
remote_exec = CMD_SEP.join([b'cd %s', b'%s server::%s'])
is_remote = False
if (src_dir and (not source_local)):
src_dir = (remote_exec ... |
class Environment(Singleton):
def __init__(self, conf_dir=None, data_path=None):
if (not hasattr(self, 'conf_dir')):
if conf_dir:
self.conf_dir = conf_dir
else:
self.conf_dir = get_platform().get_default_conf_dir()
if (not hasattr(self, 'data_p... |
def interpret_opcodes(iterable):
vl = Values()
nt = Notification()
for (kind, data) in iterable:
if (kind == TYPE_TIME):
vl.time = nt.time = data
elif (kind == TYPE_TIME_HR):
vl.time = nt.time = (data / HR_TIME_DIV)
elif (kind == TYPE_INTERVAL):
vl... |
def test_db_access_with_repr_in_report(django_pytester: DjangoPytester) -> None:
django_pytester.create_test_module("\n import pytest\n\n from .app.models import Item\n\n def test_via_db_blocker(django_db_setup, django_db_blocker):\n with django_db_blocker.unblock():\n ... |
def _safe_attr(attr, camel_killer=False, replacement_char='x'):
allowed = ((string.ascii_letters + string.digits) + '_')
attr = _safe_key(attr)
if camel_killer:
attr = _camel_killer(attr)
attr = attr.replace(' ', '_')
out = ''
for character in attr:
out += (character if (characte... |
class Effect3036(BaseEffect):
type = 'passive'
def handler(fit, skill, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Missile Launcher Bomb')), 'moduleReactivationDelay', (skill.getModifiedItemAttr('reactivationDelayBonus') * skill.level), **kwar... |
def might_extract_gz(path):
path = Path(path)
file_output_name = '.'.join(path.name.split('.')[:(- 1)])
file_name = path.name
if (not (path.parent / file_output_name).exists()):
logging.info('Extracting %s ...\n', file_name)
with gzip.open(str(path), 'rb') as f_in:
with open(... |
def get_all_preds_for_execution(gold: str, pred: str) -> Tuple[(int, Iterator[str])]:
(_, gold_values) = extract_query_values(gold)
(pred_query_value_replaced, _) = extract_query_values(pred)
num_slots = len([v for v in pred_query_value_replaced if (v == VALUE_NUM_SYMBOL.lower())])
num_alternatives = (l... |
def test_pickups_to_solve_list_multiple(echoes_game_description, echoes_pickup_database, echoes_game_patches):
db = echoes_game_description.resource_database
missile_expansion = pickup_creator.create_ammo_pickup(echoes_pickup_database.ammo_pickups['Missile Expansion'], [5], False, db)
pool = ([missile_expan... |
_loss('weighted_cross_entropy')
def weighted_cross_entropy(pred, true):
if (cfg.model.loss_fun == 'weighted_cross_entropy'):
V = true.size(0)
n_classes = (pred.shape[1] if (pred.ndim > 1) else 2)
label_count = torch.bincount(true)
label_count = label_count[label_count.nonzero(as_tupl... |
class MovingAverage():
def __init__(self, ema, oneminusema_correction=True):
self.ema = ema
self.ema_data = {}
self._updates = 0
self._oneminusema_correction = oneminusema_correction
def update(self, dict_data):
ema_dict_data = {}
for (name, data) in dict_data.ite... |
def search_molecules_in_crystal(struc, tol=0.2, once=False, ignore_HH=True):
def check_one_layer(struc, sites0, visited):
new_members = []
for site0 in sites0:
(sites_add, visited) = check_one_site(struc, site0, visited)
new_members.extend(sites_add)
return (new_membe... |
def load_img_info(files):
assert isinstance(files, tuple)
(img_file, gt_file) = files
assert (int(osp.basename(gt_file)[3:(- 4)]) == int(osp.basename(img_file)[2:(- 4)]))
img = mmcv.imread(img_file, 'unchanged')
img_info = dict(file_name=osp.join(osp.basename(img_file)), height=img.shape[0], width=i... |
class DummyLM(LM):
def __init__(self):
pass
def create_from_arg_string(cls, arg_string, additional_config=None):
return cls()
def loglikelihood(self, requests):
res = []
for _ in requests:
res.append(((- random.random()), False))
return res
def greedy_... |
def make_exe(filename):
original_mode = filename.stat().st_mode
levels = [S_IXUSR, S_IXGRP, S_IXOTH]
for at in range(len(levels), 0, (- 1)):
try:
mode = original_mode
for level in levels[:at]:
mode |= level
filename.chmod(mode)
break
... |
def convert_tensorflow(nlp: Pipeline, opset: int, output: Path):
if (not is_tf_available()):
raise Exception('Cannot convert because TF is not installed. Please install tensorflow first.')
print("/!\\ Please note TensorFlow doesn't support exporting model > 2Gb /!\\")
try:
import tensorflow ... |
_fixtures(WebFixture, CSRFFixture)
def test_submit_form_with_expired_csrf_token(web_fixture, csrf_fixture):
fixture = csrf_fixture
wsgi_app = web_fixture.new_wsgi_app(child_factory=fixture.MyForm.factory(), enable_js=True)
web_fixture.reahl_server.set_app(wsgi_app)
browser = web_fixture.driver_browser
... |
class EfficientNetMetrics():
def __init__(self, multiclass=True, weight=None, **kwargs):
self.multiclass = multiclass
self.weight = (weight is not None)
self.metrics = {}
self.metrics['loss'] = Average()
self.metrics['accuracy'] = Accuracy(is_multilabel=(not multiclass))
... |
def operate_vocab(vocab_root_path, vocab_a_name, vocab_b_name, operator):
assert (operator in ['intersect', 'sub'])
vocab_a = load_vocab((vocab_root_path / vocab_a_name))
vocab_b = load_vocab((vocab_root_path / vocab_b_name))
vocab_a_set = set(vocab_a)
vocab_b_set = set(vocab_b)
print(f'''{vocab... |
class DatoidCz(SimpleDownloader):
__name__ = 'DatoidCz'
__type__ = 'downloader'
__version__ = '0.02'
__status__ = 'testing'
__pattern__ = '
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallback', 'bool', 'Fallback to f... |
.parametrize('game', [RandovaniaGame.METROID_PRIME, RandovaniaGame.METROID_PRIME_ECHOES, RandovaniaGame.METROID_PRIME_CORRUPTION])
def test_on_preset_changed(skip_qtbot, preset_manager, game):
base = preset_manager.default_preset_for_game(game).get_preset()
preset = dataclasses.replace(base, uuid=uuid.UUID('b41... |
class CosineDecayScheduler(object):
def __init__(self, base_lr=1.0, last_iter=0, T_max=50):
self.base_lr = base_lr
self.last_iter = last_iter
self.T_max = T_max
self.cnt = 0
def decay_rate(self, step):
self.last_iter = step
decay_rate = (((self.base_lr * (1 + math... |
class uvm_tlm_fifo(uvm_tlm_fifo_base):
def __init__(self, name=None, parent=None, size=1):
super().__init__(name, parent, size)
def size(self):
return self.queue.maxsize
def used(self):
return self.queue.qsize()
def is_empty(self):
return self.queue.empty()
def is_ful... |
class SlopePupil(Pupil):
_type = 'slope'
slope = None
def __init__(self, slope, **kwargs):
super().__init__(**kwargs)
self.slope = slope
def dict(self):
dat = super().dict()
dat['slope'] = float(self.slope)
return dat
def text(self):
(yield from super(... |
class Validator(Feature):
pickle_rm_attr = ['validate', 'consistent']
def on_attach(self, fgraph):
for attr in ('validate', 'validate_time'):
if hasattr(fgraph, attr):
raise AlreadyThere('Validator feature is already present or in conflict with another plugin.')
fgrap... |
class StackedResidualBlocks(nn.Module):
def __init__(self, n_blocks: int, conv_op: Type[_ConvNd], input_channels: int, output_channels: Union[(int, List[int], Tuple[(int, ...)])], kernel_size: Union[(int, List[int], Tuple[(int, ...)])], initial_stride: Union[(int, List[int], Tuple[(int, ...)])], conv_bias: bool=Fal... |
def display_constraints(ctr_entities, separator=''):
for ce in ctr_entities:
if (ce is not None):
if hasattr(ce, 'entities'):
print((separator + str(ce)))
display_constraints(ce.entities, (separator + '\t'))
else:
print((separator + str... |
def test_saving_the_same_answer_does_not_trigger_event(submission_factory, graphql_client, user, schedule_item_factory, slot_factory, day_factory, mocker):
mock_event = mocker.patch('api.schedule.mutations.send_new_schedule_invitation_answer')
graphql_client.force_login(user)
submission = submission_factory... |
def resolve_variants(node, rng: np.random.RandomState, variant_config, output_config):
if isinstance(node, dict):
if ('_variants' in node):
var = node['_variants']
global_id = var['global_id']
if (var['type'] == 'options'):
option_dict = var['options']
... |
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